A neural network for identifying radio technologies

ABSTRACT

A computer-implemented method providing a neural network for identifying radio technologies employed in an environment. The neural network includes an autoencoder having an encoder, and a classifier. The method has the steps of sensing a radio spectrum of the environment thereby obtaining a set of data samples, labelling a subset of the data samples by a respective radio technology thereby obtaining labelled data samples, training the autoencoder in an unsupervised way by unlabelled data samples, training the classifier in a supervised way by the labelled data samples, and providing the neural network by coupling the output of an encoder network of the autoencoder to an input of the classifier.

FIELD OF THE INVENTION

The present invention generally relates to the field of identifyingradio technologies employed by nodes for operating in an environmentcomprising one or more wireless networks that share a radio spectrum.

BACKGROUND OF THE INVENTION

Radio spectrum has become extremely crowded due to the advent ofnon-collaborative radio technologies that share the same spectrum. Inthis coexisting environment, interference is one of the criticalchallenges and if unsolved, this leads to performance degradations.Recognizing or identifying a radio technology that accesses the spectrumis fundamental to define spectrum management policies to mitigateinterferences.

Cognitive radio, CR, has emerged as an enabling technology that providessupport for dynamic spectrum access, DSA. It refers to the capability ofsharing the spectrum among multiple technologies in an opportunisticmanner. One of the critical problems that DSA faces is to identify ifsome technology is accessing the same spectrum and then take appropriatemeasures to combat the performance degradation due to interference. Thisproblem is termed as the Technology Recognition, TR, problem, and itrefers to identify radio signals of wireless technologies withoutrequiring any signal pre-processing such as channel estimation, andtiming and frequency synchronization.

Traditionally, TR is done by domain experts, which use carefullydesigned hand-crafted rules to extract features from the radio signals.On the contrary, state-of-the-art approaches based on machine learningmethods may extract features directly from raw input data and performrecognition tasks on those features automatically.

However, state-of-the-art approaches for technology recognition usingmachine learning are based on supervised learning, which requires anextensive labelled data set to perform well. If the technologies andtheir environment are entirely unknown, the labelling task becomestime-consuming and challenging.

It is therefore an object of the present invention to alleviate to abovedrawback and to provide an improved solution for identifying radiotechnologies in an environment comprising one or more wireless networks.

SUMMARY OF THE INVENTION

This object is achieved, in a first aspect, by a computer-implementedmethod for providing a neural network for identifying radio technologiesemployed in an environment, the neural network comprising an autoencoderand a classifier, the method comprising the steps of:

-   -   sensing a radio spectrum of the environment thereby obtaining a        set of data samples;    -   labelling a subset of the data samples by a respective radio        technology thereby obtaining labelled data samples;    -   training the autoencoder in an unsupervised way by unlabelled        data samples;    -   training the classifier in a supervised way by the labelled data        samples; and    -   providing the neural network by coupling the output of an        encoder network of the autoencoder to the input of the        classifier.

The environment comprises a plurality of nodes which operate in theenvironment. The nodes are, for example, user terminals, access points,gateways and/or base stations. A node that belongs to a wireless networkuses one or more wireless radio technologies. In addition, a pluralityof wireless networks also exists and work independently from each other.The wireless networks may operate on a same or partially overlappingspectrum.

As a first step, the environment is scanned by sensing a radio spectrum.This is, a part of the electromagnetic spectrum which is of interest issensed on the presence of wireless signals. The sensing results in a setof data samples, which will be further processed.

The spectrum sensing is, for example, performed by capturing in-phaseand quadrature, IQ, samples and may be performed using Software DefinedRadio, SDR, platforms. Prior to further processing steps, the samplesmay, according to an embodiment, transformed depending on the model thatsubsequently will be trained. For example, the IQ samples which are timedomain representation of radio signals, may be transformed into otherdomains such as frequency or time-frequency.

Next, a part or a subset of the data samples is selected andsubsequently labelled in terms of a respective radio technology. Inother words, a part of the data samples is chosen as beingrepresentative samples of the radio technologies and labelled.Preferably, here, domain expert knowledge or in combination with pseudolabelling, among other techniques, may be used. The labelled datasamples with the associated labels may further be stored together withthe other unselected and unlabelled samples.

The labelling may be performed by indicating to which class a givencaptured or sensed data sample belongs, or such a class label may be aname of a technology, or may further be more expressive and may compriseinformation about a spectrum utilized over time, central frequencies,duty cycle, or other information that may be related to the sample.

The storage may, for example, be performed in two databases. A firstdatabase then comprises a sample database, and a second databasecomprises a label database. Data samples, for example in the form of IQsamples, are stored in the sample database, while the label database maybe used for storing the labels of a subset of the set of samples.Depending on the type of data, transformed or not, and a training step,the databases may be connected to one or more blocks.

Further, to provide the neural network a training is performed in twosteps. First, an autoencoder is trained in an unsupervised way with theunlabelled data samples. An autoencoder is a neural network that istrained to copy its input to its output. An autoencoder is composed oftwo parts, an encoder and a decoder. The weights of the trainedautoencoder are locked to preserve the important features that arelearned during the unsupervised learning step. Second, after theunsupervised learning, a classifier is trained in a supervised way usingthe labelled data samples. During the supervised learning, the encoderis used as a feature extractor. This provides an initial bootstrappingon the classification task. Optionally, a fine-tuning step may beperformed by, for example, retraining all the layers in the classifierto increase the accuracy of the resulting model. Then, when locked, theweights of the trained autoencoder may be unlocked.

Finally, after the training steps, the neural network is provided and istrained to be able to identify technologies on which it was trained forin different unknown and dynamic environments.

In the supervised learning step of the neural network, only a limitednumber of labelled data samples are needed. This makes the labellingtask less time-consuming compared to the state-of-the-art machinelearning methods for technology recognition. Thus, by this semisupervised learning approach for technology recognition by separatingthe feature extraction from the classification task in the neuralnetwork architecture, the use of unlabelled data is maximized.Furthermore, the use of domain expertise knowledge is only required whenlabelling few representative examples.

Another advantage is that even unknown radio technologies may beidentified or recognized, without needing expert knowledge for eithermodelling signals of the environment or selecting required features suchas modulation scheme, duty cycle, power level, etc., thereof

According to an embodiment, the classifier comprises the encoder and aclassification block.

The classification block is, for example, a SoftMax layer which ispreceded by convolutional and/or dense layers to increase the accuracyof the classifier. Further, a non-normalized output of the classifiermay be mapped to a probability distribution over predicted output ofradio technologies.

According to an embodiment, the autoencoder comprises a convolutionalneural network, CNN.

While traditional deep neural networks, DNNs, are built by connecting aseries of fully connected layers, a CNN connects the neurons of a givenlayer, called convolutional layer, with only a few numbers of neurons ofthe next layer to reduce the computational complexity of learning.Preferably, in this embodiment the data samples comprise IQ samples asan input. Other types of input may be used as well, such as, forexample, fast Fourier transform, FFT, samples.

According to an embodiment, the encoder comprises two convolutionallayers with rectified linear unit, ReLU, activation function, each layerfollowed by a batch normalization and a dropout layer forregularization.

Downsampling in the autoencoder may be performed by using strideconvolution or max-pooling layers. Further, the dropout layers allow theautoencoder, or preferably a deep autoencoder, DAE, to behave as adenoising DAE to improve its capacity as feature extractor.

According to an embodiment, the radio technologies comprise at least oneof the group of 5G; 5G New Radio, NR; Long Term Evolution, LTE; PrivateLTE; Citizens Broadband Radio Service, CBRS; MulteFire; LTE-LicensedAssisted Access, LTE-LAA; Narrowband-Internet of Things, NV-IoT;Enhanced machine type communication, eMTC; 802.11ax; Wi-Fi 6; 802.11ah;802.11af; 802.11p; vehicle to vehicle, V2V; vehicle to infrastructure,V21; ZiBee; Bluetooth; WiMax; GSM.

In other words, a plurality of radio technologies may be identified bythe neural network architecture. Further, besides the 5G and legacywireless technologies, the neural network may be trained to identify anytype of wireless radio technology in the radio spectrum, thus evenunknown technologies may be identified.

According to a second aspect, the invention relates to the neuralnetwork according to the method of the first aspect.

The neural network may, for example, be trained with data samplescaptured from a range of environments. This allows identifyingtechnologies in various unknown and dynamic environments.

According to a third aspect, the invention relates to acomputer-implemented method for identifying radio technologies in anenvironment by the neural network according to the second aspect.

According to an embodiment, the computer-implemented method furthercomprises the step of changing a centre frequency of one of the radiotechnologies based on the identified radio technologies.

According to an embodiment, the computer-implemented method furthercomprises the step of assigning a collision-free time slot fortransmission based on the identified radio technologies.

In other words, the computer-implemented method may employ differentstrategies to avoid a same use of the radio spectrum, and/or to make ashared use thereof in an efficient manner.

According to a fourth aspect, the invention relates to a data processingsystem comprising means for carrying out the method according to thefirst and/or third aspect.

According to a fifth aspect, the invention relates to a node foroperating in a wireless network configured to identify radiotechnologies employed in an environment by the computer-implementedmethod according to the third aspect.

According to a sixth aspect, the invention relates to a computer programproduct comprising computer-executable instructions for causing a nodeto perform at least the steps of the computer-implemented methodaccording to the third aspect.

According to a seventh aspect, the invention relates to a computerreadable storage medium comprising the computer program productaccording to the sixth aspect.

BRIEF DESCRIPTION OF THE DRAWINGS

Some example embodiments will now be described with reference to theaccompanying drawings.

FIG. 1 illustrates a semi-supervised algorithm implemented using a deepautoencoder according to an embodiment of the invention;

FIG. 2 illustrates a spectrum manager configured to recognize radiotechnologies;

FIG. 3 illustrates two wireless networks each using a different radiotechnology;

FIG. 4 illustrates time and time-frequency signatures of wirelesstechnologies;

FIG. 5 illustrates a workflow of a semi-supervised learning approachaccording to an embodiment of the invention; and

FIG. 6 shows an example embodiment of a suitable computing system forperforming one or several steps in embodiments of the invention.

DETAILED DESCRIPTION OF EMBODIMENT(S)

In FIG. 3 two networks are illustrated. A first network comprises nodes300-305 which are configured to communicate with each other through afirst radio technology. The illustration further comprises a secondnetwork comprising nodes 310-311 which are likewise configured tocommunicate with each other through a second radio technology. The nodes300-305 are not configured to communicate with the nodes 310-311,although they share a same or partially overlapping radio spectrum.Thus, both networks interfere and compete. Other networks may be presentas well, which likewise compete and interfere. Thus, FIG. 3 illustratesan environment 320 wherein different radio technologies are present forwireless communication purposes. Different radio technologies arefurther illustrated in FIG. 2. The nodes or agents 200-203 eachrepresent a radio technology which may operate in the environment 320.

A radio technology may further be illustrated through a time andtime-frequency signatures of the wireless technologies to be recognized.This is illustrated in FIG. 4 wherein two distinct 401 and 402 radiotechnologies are illustrated. Radio technology 401 is, for example,deployed by nodes 300-305 and radio technology 402 is deployed by nodes310-311.

A spectrum manager 210 will identify the different radio technologies200-203 operating in the environment 320. The results of the spectrummanager 210, thus the technology recognition may then be used by makingspectrum decisions 211. The goal of the spectrum manager 210 is toassist the unknown wireless technologies 200-203 to make spectrumdecisions 211 by first identifying them and then doing frequency domainanalysis. In order to enable this, the spectrum manager 210 executes thefollowing tasks in the listed manner: training 214, validation 213,frequency domain analysis 212, and spectrum decision 211. In thisillustrative embodiment, the focus will now be on the training 214 andvalidation 213 steps to enable technology recognition for cognitiveradio systems.

The training 214 task is used to train a model in a semi-supervised 215way with raw in-phase and quadrature, IQ, samples of a number of radios200-203 using a deep autoencoder, DAE. Further, once the model istrained 214, in the validation task 213, it may identify the unknownwireless technologies 200-203. In the frequency domain analysis task212, frequency domain analysis of the identified technologies 200-203 isdone by extracting spectrum occupancy information of the technologies200-203. Finally, in the spectrum decision task 211, the radio uses theextracted spectrum efficiency information to define actions, such aschange the frequencies of the radios 200-203 and/or assign acollision-free time slot for transmissions, so that a fair coexistencemay be realized. Once the spectrum decisions are made, they are notifiedto the radios 200-203 via, for example, control channels.

To formulate a technology recognition problem, a communication system inwhich a received signal r(t) may be represented as follows:

r(t)=s(t)*h(t)+ω(t)   (Eq. 1),

wherein s(t) is the original transmitted signal, h(t) is the timevarying impulse response of the transmit channel, and ω(t) representsadditive white gaussian noise, AWGN, with zero mean and variance σ². Inmodern digital communication systems, the transmitted signal s(t) ismodelled as follows:

s(t)=I(t)cos(2πf _(c) t)+Q(t)sin(2πf _(c) t), s(t)=i(t)+jq(t)   (Eq. 2),

where s(t) is called quadrature signal or IQ samples, and the i(t) andq(t) are termed as the in-phase and quadrature components, respectively.

Given a classification problem with an input vector set X and theircorresponding target variables set Y, the objective is to find afunction f that predicts y∈Y given a new value for x∈X, where yrepresents L class labels:

$\begin{matrix}{\left. {f:{\mathbb{R}}^{n}}\rightarrow 1 \right.,\ldots,L} & \left( {{Eq}.3} \right)\end{matrix}$ y = f(x).

Let X={x₁, x₂, . . . , x_(N)} and Y={y₁, y₂, . . . , y_(N)} be a set ofN examples of radio technologies and their corresponding labels,respectively, where x_(i)∈X and y_(i)∈Y for all i∈[N]:={1,2, . . . , N}.By semi-supervised learning, SSL, the set X is divided in two subsetsX_(s)={x₁, x₂, . . . , X_(L)}, for which their corresponding labelsY_(s)={y₁, y₂, . . . , y_(L)} are provided, and X_(u)={x_(L+1), . . . ,x_(N)}, for which no labels are provided such that X={x₁, x₂, . . . ,x_(L), x_(L+1), . . . , x_(N)}.

To use SSL algorithms for recognition, it is further required that theknowledge acquired about the distribution of the examples from theunlabelled data set, i.e., p(x), is useful to infer p(y|x). Otherwise,semi-supervised learning may decrease the performance of the supervisedclassifier by misguiding it during the learning process. SSL usesunlabelled data to learn valuable information about the data, and thenuses it to finetune a classifier with a reduced number of labels.Through the invention, the technology recognition system can be usedeven when the environment 320 is entirely unknown and no information isprovided at all.

Through sensing and capturing over-the-fly radio signals in the form ofIQ samples is performed using Software Defined Radio, SDR, platforms.Next, by the invention the feature extraction is decoupled viaunsupervised learning, and the classification tasks via supervisedlearning while keeping the high expressiveness of deep learning, DL,models. The overall workflow of the semi-supervised learning approach bythe invention is illustrated in FIG. 5.

[51] In a first step 500, the spectrum is sensed by capturing IQ sampleswhich are further processed by subsequent steps 501-505. Next, dependingon the model to be trained, the original IQ samples, which are timedomain representation of radio signals may be transformed 501 into otherdomains, such as frequency or time-frequency. When IQ samplesrepresentation are further used no further processing is required.

In the next step 502, the data is labelled. In this step, two sub stepsare performed, namely samples selection and labelling of the samples.The architecture of the invention is semi-supervised, thus making itimportant to select representative samples of the radio technologiesthat needs to be identified. Here, domain expert knowledge or incombination with pseudo labelling may be used. The samples and thelabels associated with the labelled samples are further stored 503.

This data storage 503 block comprises two databases, namely a sampledatabase and a label database. IQ samples are stored in the sampledatabase, while the label database is used for storing labels of areduced set of examples. Depending on the kind of data and the trainingstrategy, the databases are connected to one or more blocks, namely thesupervised learning 510 and the unsupervised learning 511, and the batchsystem 512.

In the offline training, the input data is created by selecting aportion of the data from the sample database via a predefined strategy,for example uniform random selection.

Next, in the batch system for online training 512, on the other hand,the input may be provided by a batch system that takes data from thesample database 503 and uses it for retraining a model.

The semi-supervised technology recognition classification block 504receives the sensed data and performed the classification task. Theblock 504 also receives a limited labelled data set from the datalabelling system block 502. Based on the labelled and unlabelled datasets, different learning algorithms may be used in the supervised 510and unsupervised 511 learning blocks, and how they interact to performthe SSL task.

Finally, in the technology recognition block 505 the proposedarchitecture indicates which class a given capture sample belongs to.This may, for example, be the name of the technology, but may also bemore expressive and comprises information about spectrum utilized overtime, central frequencies, duty cycle, etc.

The proposed workflow of the invention is flexible to support a range ofSSL algorithms, training methods, and input types. The selection of thesemi-supervised approach mainly depends on various factors including theamount of available data, the number of labels, the complexity of theradio signals to be identified, and the need for offline or onlinetraining capabilities, etc.

The SSL TR block illustrated in FIG. 5 may be implemented using a DAE130 as illustrated in FIG. 1. The DAE 130 is composed of two parts, anencoder 120 that maps h=f (x), where h is known as the code, and adecoder 121 that produces a reconstruction r=g(h).

As an input 110 for the DAE 130 IQ samples or any transformation of theradio signals of the different radio technologies are provided. Next,the encoder 120 comprises a first convolutional layer 101, for examplewith a 3×3 filter kernel, 64 feature maps, 4×4 strides and a dropout of0.4. The second convolutional layer 102 comprises a 3×3 filter kernel,64 feature maps, 4×4 strides and a dropout of 0.4. Next, there is afully connected 1×125 neurons layer 103. Next, there is a firsttranspose convolutional layer 104 comprising a 3×3 filter kernel, 64feature maps, 1×4 strides and a dropout of 0.4, and a second transposeconvolutional layer 105 comprising a 3×3 filter kernel, 64 feature maps,1×4 strides and a dropout of 0.4. The output 112 of the DAE 120-121 isfurther used by the encoder 123 which comprises a fully connected 1×128neurons 106 and a Softmax layer 107 comprising 1×17 neurons. The numberof convolutional layers, feature maps, strides, dropout, filter size,etc are termed as hyperparameters in machine learning terms and for eachspecific case a different combination of them may be used. The modellingby the DAE 120-121 is performed through unsupervised learning withunlabelled examples and by the encoder 123 through supervised learningwith representative labelled examples. The specific parameters of eachlayer, etc., may be determined using a hyperparameter swapping. Theencoder configuration of the invention generates an intermediate code ofsize 128, e.g., a reduction factor of 16x. Similarly, the decoder partfollows the same pattern but in reverse order and replacingconvolutional layers by transposed convolutional layers. The DAE 130comprises 1M of trainable parameters. The autoencoder is trained byusing batches of size 128, the Adam optimizer with a learning rate of0.0004, and binary cross-entropy as the loss function forreconstruction. The supervised part of the architecture is composed ofthe encoder part of the DAE in addition to two dense layers, one with128 neurons, and the second one with 17 neurons and a SoftMax activationlayer for classification. The resulting model has 500 k and 18 ktrainable parameters in phase 1 and phase 2, respectively. The model istrained using the same parameters as the DAE except that the lossfunction is categorical cross-entropy and the learning rate is reducedto 0.004. Finally, the output 111 is generated.

Thus, differently formulated, for SSL, the DAE 130 provides a two-steptraining process. First, the DAE 130 which is composed of the encoder120 and the decoder 21 in an unsupervised way using only X_(u).Secondly, after the unsupervised learning, a training is performed by aclassifier 123 using an encoder 106 together with a Softmax classifier107 in a supervised way using the reduced labelled data set X_(s).

During the supervised training, the encoder 106 is used as a featureextractor for the Softmax classifier 107. This step provides an initialbootstrapping on the classification task. Then, a fine-tune step isperformed, this is, all layers in 123 are retrained in order to increasethe accuracy of the resulting model.

FIG. 6 shows a suitable computing system 600 enabling to implementembodiments of the method for identifying radio technologies in anenvironment according to the invention. Computing system 600 may ingeneral be formed as a suitable general-purpose computer and comprise abus 610, a processor 602, a local memory 604, one or more optional inputinterfaces 614, one or more optional output interfaces 616, acommunication interface 612, a storage element interface 606, and one ormore storage elements 608. Bus 610 may comprise one or more conductorsthat permit communication among the components of the computing system600. Processor 602 may include any type of conventional processor ormicroprocessor that interprets and executes programming instructions.Local memory 604 may include a random-access memory (RAM) or anothertype of dynamic storage device that stores information and instructionsfor execution by processor 602 and/or a read only memory (ROM) oranother type of static storage device that stores static information andinstructions for use by processor 602. Input interface 614 may compriseone or more conventional mechanisms that permit an operator or user toinput information to the computing device 600, such as a keyboard 620, amouse 630, a pen, voice recognition and/or biometric mechanisms, acamera, etc. Output interface 616 may comprise one or more conventionalmechanisms that output information to the operator or user, such as adisplay 640, etc. Communication interface 612 may comprise anytransceiver-like mechanism such as for example one or more Ethernetinterfaces that enables computing system 600 to communicate with otherdevices and/or systems, for example with other one or more of the nodes300-305 or 310-311. The communication interface 612 of computing system600 may be connected to such another computing system by means of alocal area network (LAN) or a wide area network (WAN) such as forexample the internet. Storage element interface 606 may comprise astorage interface such as for example a Serial Advanced TechnologyAttachment (SATA) interface or a Small Computer System Interface (SCSI)for connecting bus 910 to one or more storage elements 608, such as oneor more local disks, for example SATA disk drives, and control thereading and writing of data to and/or from these storage elements 908.Although the storage element(s) 608 above is/are described as a localdisk, in general any other suitable computer-readable media such as aremovable magnetic disk, optical storage media such as a CD or DVD, -ROMdisk, solid state drives, flash memory cards, . . . could be used.Computing system 600 could thus correspond to a node in the embodimentsillustrated by FIG. 2 or FIG. 3.

As used in this application, the term “circuitry” may refer to one ormore or all of the following:

(a) hardware-only circuit implementations such as implementations inonly analog and/or digital circuitry and

(b) combinations of hardware circuits and software, such as (asapplicable):

-   -   (i) a combination of analog and/or digital hardware circuit(s)        with software/firmware and    -   (ii) any portions of hardware processor(s) with software        (including digital signal processor(s)), software, and        memory(ies) that work together to cause an apparatus, such as a        mobile phone or server, to perform various functions) and

(c) hardware circuit(s) and/or processor(s), such as microprocessor(s)or a portion of a microprocessor(s), that requires software (e.g.firmware) for operation, but the software may not be present when it isnot needed for operation.

This definition of circuitry applies to all uses of this term in thisapplication, including in any claims. As a further example, as used inthis application, the term circuitry also covers an implementation ofmerely a hardware circuit or processor (or multiple processors) orportion of a hardware circuit or processor and its (or their)accompanying software and/or firmware. The term circuitry also covers,for example and if applicable to the particular claim element, abaseband integrated circuit or processor integrated circuit for a mobiledevice or a similar integrated circuit in a server, a cellular networkdevice, or other computing or network device.

Although the present invention has been illustrated by reference tospecific embodiments, it will be apparent to those skilled in the artthat the invention is not limited to the details of the foregoingillustrative embodiments, and that the present invention may be embodiedwith various changes and modifications without departing from the scopethereof. The present embodiments are therefore to be considered in allrespects as illustrative and not restrictive, the scope of the inventionbeing indicated by the appended claims rather than by the foregoingdescription, and all changes which come within the scope of the claimsare therefore intended to be embraced therein.

It will furthermore be understood by the reader of this patentapplication that the words “comprising” or “comprise” do not excludeother elements or steps, that the words “a” or “an” do not exclude aplurality, and that a single element, such as a computer system, aprocessor, or another integrated unit may fulfil the functions ofseveral means recited in the claims. Any reference signs in the claimsshall not be construed as limiting the respective claims concerned. Theterms “first”, “second”, third”, “a”, “b”, “c”, and the like, when usedin the description or in the claims are introduced to distinguishbetween similar elements or steps and are not necessarily describing asequential or chronological order. Similarly, the terms “top”, “bottom”,“over”, “under”, and the like are introduced for descriptive purposesand not necessarily to denote relative positions. It is to be understoodthat the terms so used are interchangeable under appropriatecircumstances and embodiments of the invention are capable of operatingaccording to the present invention in other sequences, or inorientations different from the one(s) described

1.-13. (canceled)
 14. A computer-implemented method for providing aneural network for identifying radio technologies employed in anenvironment, the neural network comprising an autoencoder having anencoder, and a classifier, the method comprising the steps of: sensing aradio spectrum of the environment thereby obtaining a set of datasamples; labelling a subset of the data samples by a respective radiotechnology thereby obtaining labelled data samples; training theautoencoder in an unsupervised way by unlabelled data samples; trainingthe classifier in a supervised way by the labelled data samples; andproviding the neural network by coupling the output of an encodernetwork of the autoencoder to the input of the classifier.
 15. Thecomputer-implemented method according to claim 14, wherein the set ofdata samples comprises in-phase and quadrature, IQ, samples.
 16. Thecomputer-implemented method according to claim 15, further comprisingthe step of: transforming the IQ samples from a time domain to afrequency domain.
 17. The computer-implemented method according to claim14, wherein the classifier comprises the encoder and a classificationblock.
 18. The computer-implemented method according to claim 14,wherein the autoencoder comprises a convolutional neural network. 19.The computer-implemented method according to claim 14, wherein theencoder comprises two convolutional layers with rectified linear unit,ReLU, activation function, each layer followed by a batch normalizationand a dropout layer for regularization.
 20. The computer-implementedmethod according to claim 14, wherein the radio technologies comprise atleast one of the group of 5G; 5G New Radio, NR; Long Term Evolution,LTE; Private LTE; Citizens Broadband Radio Service, CBRS; MulteFire;LTE-Licensed Assisted Access, LTE-LAA; Narrowband-Internet of Things,NV-IoT; Enhanced machine type communication, eMTC; 802.11ax; Wi-Fi 6;802.11ah; 802.11af; 802.11p; vehicle to vehicle, V2V; vehicle toinfrastructure, V2I; ZiBee; Bleutooth; WiMax; GSM.
 21. Acomputer-implemented method comprising identifying radio technologiesemployed in an environment by a neural network obtained by claim
 14. 22.The computer-implemented method according to claim 21, furthercomprising the step of: changing a centre frequency of one of the radiotechnologies based on the identified radio technologies.
 23. Thecomputer-implemented method according to claim 21, further comprisingthe step of: assigning a collision-free time slot for transmission basedon the identified radio technologies.
 24. A data processing systemcomprising means for carrying out the method according to claim
 14. 25.A computer program product comprising computer-executable instructionsfor causing a data processing system to perform at least the steps ofthe computer-implemented method according to claim
 14. 26. A computerreadable storage medium comprising the computer program productaccording to claim 25.